Pano-GAN: A Deep Generative Model for Panoramic Dental Radiographs by Søren Pedersen, Sanyam Jain, Mikkel Chavez, Viktor Ladehoff, Bruna Neves de Freitas, Ruben Pauwels

“…Significant data cleaning and preprocessing were conducted to standardize the input formats while maintaining anatomical variability. Four candidate models were identified by varying the critic iterations, number of features and the use of denoising prior to training. …”
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Biological features and medical significance of the <i>Listeria</i> bacteria by I. A. Derevyanchenko, Lyudmila A. Kraeva

“…The increasing role of Listeria in the pattern of human and animal infectious pathologies, the variability of their morphological, cultivable and biochemical properties, as well as the constant modification of the surface Listeria antigens underlies a need to improve listeriosis diagnostics and requires creation of new immunobiological preparations and modern regimens for isolation and identification of various Listeria types. …”
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Attentive Self-supervised Contrastive Learning (ASCL) for plant disease classification by Getinet Yilma, Mesfin Dagne, Mohammed Kemal Ahmed, Ravindra Babu Bellam

“…Despite involving fewer than 17 classes, the high variability within each class, such as the disease progression stages, underscores the fine-grained nature of the classification task. …”
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AI and Data Analytics in the Dairy Farms: A Scoping Review by Osvaldo Palma, Lluis M. Plà-Aragonés, Alejandro Mac Cawley, Víctor M. Albornoz

“…In the treatment of variability, the models reviewed are mostly deterministic (77%), and the stochastic models (5%) are considered in a small number of cases. …”
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Innovative approaches for skin disease identification in machine learning: A comprehensive study by Kuldeep Vayadande, Amol A. Bhosle, Rajendra G. Pawar, Deepali J. Joshi, Preeti A. Bailke, Om Lohade

“…For these illnesses to be managed and treated effectively, prompt and correct diagnosis is essential, yet it often presents a challenge due to the subjective nature of visual examination and the variability in clinical presentations. The field of dermatology has seen a change in recent years due to the convergence of artificial intelligence and medicine, which has produced creative methods for computer-aided diagnostics. …”
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Improving Cell Nuclei Segmentation in Pathological Tissues Using Self-Supervised Regression Method by Hesham Ali, Mostafa Hammouda, Mustafa Elattar, Sahar Selim

“…Traditionally, WSIs are manually examined&#x2014;a process that is not only time-consuming but also subject to observer variability. This analysis typically focuses on the assessment of nuclei shape and size, crucial indicators of cancer stage and progression. …”
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Hybrid transformer-CNN and LSTM model for lung disease segmentation and classification by Syed Mohammed Shafi, Sathiya Kumar Chinnappan

“…The improved accuracy achieved by the L-MLSTM model highlights its capability to better handle the complexity and variability in lung images. This hybrid approach enhances the model’s ability to distinguish between different types of lung diseases and reduces diagnostic errors compared to existing methods.…”
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Artificial Intelligence-Based Models for Automated Bone Age Assessment from Posteroanterior Wrist X-Rays: A Systematic Review by Isidro Miguel Martín Pérez, Sofia Bourhim, Sebastián Eustaquio Martín Pérez

“…Traditional methods—Greulich–Pyle atlas and Tanner–Whitehouse scoring—are time-consuming, operator-dependent, and prone to inter- and intra-observer variability. Aim: To systematically review the performance of AI-based models for automated bone-age estimation from left PA hand–wrist radiographs. …”
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PlantView: Integrating deep learning with 3D modeling for indoor plant augmentation by Sitara Afzal, Haseeb Ali Khan, Jong Weon Lee

“…Indoor plant recognition poses significant challenges due to the variability in lighting conditions, plant species, and growth stages. …”
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Task-Driven Real-World Super-Resolution of Document Scans by Maciej Zyrek, Tomasz Tarasiewicz, Jakub Sadel, Aleksandra Krzywon, Michal Kawulok

“…Although recent deep learning-based methods have demonstrated notable success on simulated datasets—with low-resolution images obtained by degrading and downsampling high-resolution ones—they frequently fail to generalize to real-world settings, such as document scans, which are affected by complex degradations and semantic variability. In this study, we introduce a task-driven, multi-task learning framework for training a super-resolution network specifically optimized for optical character recognition tasks. …”
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Reducing lead requirements for wearable ECG: Chest lead reconstruction with 1D-CNN and Bi-LSTM by Kazuki Hebiguchi, Hiroyoshi Togo, Akimasa Hirata

“…While increasing the number of input leads enhanced reconstruction accuracy and reduced variability, the improvements plateaued beyond the use of double input leads. …”
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Enhancing chronic wound assessment through agreement analysis and tissue segmentation by Ana C. Morgado, Rafaela Carvalho, Ana Filipa Sampaio, Maria J. M. Vasconcelos

“…However, the current manual process of tissue segmentation and quantification, which is an indicator of the healing progress, is time-consuming and subject to variability, so automated methods that can effectively monitor wound healing are required. …”
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Accurate Sugarcane Detection and Row Fitting Using SugarRow-YOLO and Clustering-Based Spline Methods for Autonomous Agricultural Operations by Guiqing Deng, Fangyue Zhou, Huan Dong, Zhihao Xu, Yanzhou Li

“…In addition to addressing the problem of large variability in row spacing and plant spacing of sugarcane, this paper introduces the DBSCAN clustering algorithm and combines it with a smooth spline curve to fit the crop rows in order to realize the accurate extraction of crop rows. …”
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Noninvasive Continuous Glucose Monitoring Using Multimodal Near-Infrared, Temperature, and Pressure Signals on the Earlobe by Jongdeog Kim, Bong Kyu Kim, Mi-Ryong Park, Hyoyoung Cho, Chul Huh

“…Across all participants, results showed 90.9% CEG A-Zone accuracy and a MARD of 8.4%, with performance variations linked to individual factors such as earlobe thickness variability and physical activity. These outcomes demonstrate the potential of the MW-SE-NIRS system for noninvasive glucose monitoring and highlight the importance of future work on personalized modeling, sensor optimization, and larger-scale clinical validation.…”
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IoT-Enhanced Smart Parking Management With IncepDenseMobileNet for Improved Classification by Xiaoxia Zheng, Wenxi Feng, Ning Wang, Huhemandula

“…Advanced preprocessing techniques, such as Temporal Variability Adjustment and Harmonic Noise Compensation, enhanced data quality, while Proportional Adaptive Balancing and Augmentation (PABA) addressed class imbalance. …”
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AI-driven smart agriculture using hybrid transformer-CNN for real time disease detection in sustainable farming by Zhuo Zeng, Tariq Mahmood, Yu Wang, Amjad Rehman, Muhammad Akram Mujahid

“…This study introduces the AttCM-Alex model, a novel deep-learning framework designed to boost the detection and classification of plant diseases under challenging environmental conditions. By integrating convolutional operations with self-attention mechanisms, AttCM-Alex effectively addresses the variability in light intensity and image noise, ensuring robust performance. …”
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Advances in ECG and PCG-based cardiovascular disease classification: a review of deep learning and machine learning methods by Asmaa Ameen, Ibrahim Eldesouky Fattoh, Tarek Abd El-Hafeez, Kareem Ahmed

“…A pressing necessity exists for a study on the variability of these factors and their impact on cardiovascular disease (CVD). …”
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Randomization-Driven Hybrid Deep Learning for Diabetic Retinopathy Detection by A. M. Mutawa, G. R. Hemalakshmi, N. B. Prakash, M. Murugappan

“…Despite advances, existing diagnostic methods face challenges such as resource dependency, variability in accuracy, and limited accessibility, especially in underserved regions. …”
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I-AIR: intention-aware travel itinerary recommendation via multi-signal fusion and spatiotemporal constraints by Xiao Cui, Zhihua Wang, Ping Li, Qiang Xu

“…Although traditional methods have made progress in next-POI prediction and route planning, they often rely on static user preferences and overly simplistic spatial assumptions, overlooking contextual variability and non-linear distance effects. Additionally, many approaches decouple POI selection from itinerary construction and depend heavily on limited behavioral signals, failing to capture richer feedback such as ratings, dwell times, or click histories. …”
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Towards consistently measuring and monitoring habitat condition with airborne laser scanning and unmanned aerial vehicles by W. Daniel Kissling, Yifang Shi, Jinhu Wang, Agata Walicka, Charles George, Jesper E. Moeslund, France Gerard

“…Key challenges include variability in sensor characteristics and survey designs, non-transparent pre-processing workflows, heterogeneous and complex data, issues with the robustness of metrics and indices, limited model generalizability and transferability across sites, and difficulties in handling big data, such as managing large volumes and utilizing parallel or distributed computing. …”
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